High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK)
The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-dri...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/12/2/20 |
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| author | Elisabeta Cristina Timis Horia Hangan Vasile Mircea Cristea Norbert Botond Mihaly Michael George Hutchins |
| author_facet | Elisabeta Cristina Timis Horia Hangan Vasile Mircea Cristea Norbert Botond Mihaly Michael George Hutchins |
| author_sort | Elisabeta Cristina Timis |
| collection | DOAJ |
| description | The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m<sup>3</sup>/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model. |
| format | Article |
| id | doaj-art-8a894510d0c04d1f8dc24231c2550ca0 |
| institution | DOAJ |
| issn | 2306-5338 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hydrology |
| spelling | doaj-art-8a894510d0c04d1f8dc24231c2550ca02025-08-20T03:12:05ZengMDPI AGHydrology2306-53382025-01-011222010.3390/hydrology12020020High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK)Elisabeta Cristina Timis0Horia Hangan1Vasile Mircea Cristea2Norbert Botond Mihaly3Michael George Hutchins4Department of Chemical Engineering, Computer Aided Process Engineering Research Centre, “Babes-Bolyai” University, Cluj-Napoca, 11 Arany Janos, 400028 Cluj, RomaniaDepartment of Chemical Engineering, Computer Aided Process Engineering Research Centre, “Babes-Bolyai” University, Cluj-Napoca, 11 Arany Janos, 400028 Cluj, RomaniaDepartment of Chemical Engineering, Computer Aided Process Engineering Research Centre, “Babes-Bolyai” University, Cluj-Napoca, 11 Arany Janos, 400028 Cluj, RomaniaDepartment of Chemical Engineering, Computer Aided Process Engineering Research Centre, “Babes-Bolyai” University, Cluj-Napoca, 11 Arany Janos, 400028 Cluj, RomaniaUK Centre for Ecology and Hydrology Wallingford, Oxford OX10 8BB, UKThe forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m<sup>3</sup>/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model.https://www.mdpi.com/2306-5338/12/2/20pollutant transport forecasthydrological modelartificial neural networksriver flow forecastin-river phosphorus modelhigh-resolution model |
| spellingShingle | Elisabeta Cristina Timis Horia Hangan Vasile Mircea Cristea Norbert Botond Mihaly Michael George Hutchins High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) Hydrology pollutant transport forecast hydrological model artificial neural networks river flow forecast in-river phosphorus model high-resolution model |
| title | High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) |
| title_full | High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) |
| title_fullStr | High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) |
| title_full_unstemmed | High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) |
| title_short | High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK) |
| title_sort | high resolution flow and phosphorus forecasting using ann models catering for extremes in the case of the river swale uk |
| topic | pollutant transport forecast hydrological model artificial neural networks river flow forecast in-river phosphorus model high-resolution model |
| url | https://www.mdpi.com/2306-5338/12/2/20 |
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